Intelligent inspection has become a hot spot in the development of security inspection. Currently, as the Internet technology has become widely popular and the cloud computing has been applied to various industries, intelligent security inspection has attracted more and more attention of customs globally. The intelligent security inspection can not only provide clients with faster and more convenient services and improved security inspection efficiency, but also offer more valuable information to customs inspectors while achieving an enhanced seizure rate, and is thus currently one of major approaches for vendors to increase values of their products. One of such intelligent schemes is to use customs declaration/manifest data (referred to as customs declaration hereinafter) and perform an image-declaration comparison by means of image processing and semantic interpretation, so as to find out false or concealed declarations.
However, since the development of this technique is still at its initial phase, the schemes are not mature and the algorithms or software systems cannot fully satisfy users' requirements. For example, it has been proposed to use customs declaration information and perform comparison with the customs declaration by means of image matching. However, this technique is too idealistic and has poor effects in practice. It is difficult to be applied to situations where there are severe non-rigid deformations or perspective superimpositions in perspective images. It is also difficult to be applied to real-time processing of a large number of categories. Further, with big-data inference, image classification algorithms can be used for analyzing and comparing customs declarations. However, this solution has a limited effect when there are a large number of categories.
Therefore, the effects of the conventional solutions for customs declaration comparison algorithms may depend on various factors, such as a larger number of categories, regional differences between categories, self-learning for new categories, a large difference within a category, a difference between performances of different devices, and discrimination between image regions when multiple goods are contained in one container and when there is a perspective overlap. The conventional solutions do not consider these factors and thus cannot satisfy user's requirements in practice.